
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | September 13, 2019 |
Latest Amendment Date: | September 13, 2019 |
Award Number: | 1911191 |
Award Instrument: | Standard Grant |
Program Manager: |
Ann Von Lehmen
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2019 |
End Date: | September 30, 2023 (Estimated) |
Total Intended Award Amount: | $354,291.00 |
Total Awarded Amount to Date: | $354,291.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1000 E VICTORIA ST CARSON CA US 90747-0001 (310)243-2852 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1000 E Victoria St Carson CA US 90747-0001 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Networking Technology and Syst |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Emerging data- and communication-intensive applications such as online video streaming, social media, health and medical applications, scientific applications including high-energy physics and bioinformatics, and educational applications (such as the rapidly-growing Massive Open Online Courses) are all enabled by large cloud data centers. However, this underpinning infrastructure is increasingly stressed by the growing complexities of managing data center resources. This is evidenced by the frequent outages of cloud services from leading tech companies, including Amazon Cloud Storage and Microsoft Skype, and popular mobile apps such as Gmail and Whatsapp. To address this challenge, this project will create an optimal and efficient resource allocation framework for policy driven data centers (PDDCs), to manage cloud user applications and cloud resources (i.e., servers, networks, and power) in an integrated fashion.
The goal of this project is to integrate compute, data, and middleboxes (MBs), three building blocks of PDDCs, into one framework to achieve optimal cloud resource management. A variety of important problems in PDDCs, including virtual machine (VM) migration and placement, load balancing, flow priority and fault tolerance can all be solved using network flow techniques that provide optimal and efficient resource allocation solutions. In particular, the project identifies a series of new policy-preserving problems that adaptively coordinate compute, data, and MBs, and invents a suite of policy-preserving algorithms that satisfy diverse cloud policies while consuming cloud resources efficiently. The proposed techniques include placing, migrating, replicating, and traffic engineering compute, data, and MBs in the PDDC. The project will compare results with integer linear programming (ILP)-based solutions and extend the approach to multi-objective optimization problems. Expected outcomes are fundamental theories, architectures, algorithms, and protocols for the PDDCs, and prototypes that provide long term policy-preserving cloud services.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this project is to create an optimal and efficient resource allocation framework for policy driven data centers (PDDCs), to manage cloud user applications and cloud resources (i.e., servers, networks, and power) in an integrated fashion. Cloud data centers provides the infrastructure to (and underpines) many modern information technology applications such as online video streaming, social media, health and medical applications, scientific applications including high-energy physics and bioinformatics, and educational applications (such as the rapidly-growing Massive Open Online Courses). However, this underpinning infrastructure is increasingly stressed by the growing complexities of managing data center resources.
In PDDCs, network devices called middleboxes (MBs) or virtual network functions (VNFs) are introduced inside the data centers to achieve performance and security guarantees for the data center applications. One challenge is how to integrate compute, data, and three building blocks of PDDCs, into one framework to achieve optimal cloud resource management.
In this project we mainly take a network flow perspective that models the information flow in PDDCs as flows in flow networks. We show that a variety of important problems in PDDCs, including virtual machine (VM) migration and placement, load balancing, flow priority and fault tolerance can all be solved using network flow techniques that provide optimal and efficient resource allocation solutions. In particular, the project identified and solved a series of new policy-preserving problems that adaptively coordinate compute, data, and MBs, and invents a suite of policy-preserving algorithms that satisfy diverse cloud policies while consuming cloud resources efficiently. The proposed techniques include placing, migrating, replicating, and traffic engineering compute, data, and MBs in the PDDC. The outcomes include a few new theories, algorithms, and protocols for the PDDCs, published as ten reputable conference paper including Infocom, IPDPS, ICC, and Globecom.
Last Modified: 10/31/2023
Modified by: Bin Tang
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